Gemma 4 26B A4B vs Qwen3.7 Plus
Compare Gemma 4 26B A4B and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.
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Gemma 4 26B A4B vs Qwen3.7 Plus Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | Gemma 4 26B A4B | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Apr 2026 | — |
| Context Window | 256K | — |
| Parameters | 25.2B | |
| License | Apache 2.0 | |
| Pricing per 1M tokens | ||
| Input $/1M | $0.070 | $0.320 |
| Output $/1M | $0.340 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Vision Language | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 67.4% |
| Avg cost / sample | – | $0.0008 |
| Avg speed / sample | – | 7.01s |
| By task | ||
| Object Detection | – | 60.1% $0.0013 |
| Counting | – | 50.0% $0.0004 |
| Identification | – | 84.4% $0.0003 |
| OCR | – | 86.5% $0.0009 |
| Data Extraction | – | 83.5% $0.0004 |
| Reasoning (low) | – | 39.7% $0.0003 |
| Reasoning (high) | – | 68.2% $0.0043 |
Gemma 4 26B A4B vs Qwen3.7 Plus: Overview
Gemma 4 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.
For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.
Frequently Asked Questions
Gemma 4 26B A4B has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.